A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine-Reference-Cited by-同舟云学术

A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine

Published:2018-02 Issue: Volume:116 Page:169-175
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Gao Q.W.,Liu W.Y.^ORCID,Tang B.P.,Li G.J.

Funder

National Natural Science Foundation of China

Jiangsu Province

Natural Science Foundation of Jiangsu Province

Graduate Student Innovation Project of Jiangsu Province and Jiangsu Normal University of China

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference22 articles.

1. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: a review[J];Liu;Renew. Sustain. Energy Rev.,2015

2. A multiple observers and dynamic weighting ensembles scheme for diagnosing new class faults in wind turbines[J];Razavi-Far;Control Eng. Pract.,2013

3. A benchmark evaluation of fault tolerant wind turbine control concepts[J];Odgaard;IEEE Trans. Control Syst. Technol.,2015

4. Amplitude and frequency demodulation analysis for fault diagnosis of planet bearings[J];Feng;J. Sound Vib.,2016

5. An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine[J];Wang;Appl. Acoust.,2014

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